A monthly crude oil spot price forecasting model using relative inventories

Ye, Michael; Zyren, John; Shore, Joanne

doi:10.1016/j.ijforecast.2005.01.001

Cited by 125 publications

(63 citation statements)

References 7 publications

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“…Since the government oil stocks tend to be constant in the short-run, the relative level of government oil stocks (RGS) can be obtained by simply removing the trend component. Ye et al (2002Ye et al ( ), (2005 and (2007) In the pure time series framework, two models, which are particularly useful for forecasting oil prices in the long-run, are proposed by Pindyck (1999) and Radchenko (2005).…”

Section: Structural and Time Series Modelsmentioning

confidence: 99%

Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting

Bastianin

Manera

Markandya

et al. 2014

The Interrelationship Between Financial and Energy Markets

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting SummaryThe relevance of oil in the world economy explains why considerable effort has been devoted to the development of different types of econometric models for oil price forecasting. Several specifications have been proposed in the economic literature. Some are based on financial theory and concentrate on the relationship between spot and futures prices ("financial" models). Others assign a key role to variables explaining the characteristics of the physical oil market ("structural" models). The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several alternative econometric specifications proposed in the literature to capture the dynamics of oil prices. Second, we analyse the effects of different data frequencies on the coefficient estimates and forecasts obtained using each selected econometric specification. Third, we compare different models at different data frequencies on a common sample and common data. Fourth, we evaluate the forecasting performance of each selected model using static and dynamic forecasts, as well as different measures of forecast errors. Finally, we propose a new class of models which combine the relevant aspects of the financial and structural specifications proposed in the literature ("mixed" models). Our empirical findings can be summarized as follows. Financial models in levels do not produce satisfactory forecasts for the WTI spot price. The financial error correction model yields accurate in-sample forecasts. Real and strategic variables alone are insufficient to capture the oil spot price dynamics in the forecasting sample. Our proposed mixed models are statistically adequate and exhibit accurate forecasts. Different data frequencies seem to affect the forecasting ability of the models under analysis.

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Section: Structural and Time Series Modelsmentioning

confidence: 99%

Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting

Bastianin

Manera

Markandya

et al. 2014

The Interrelationship Between Financial and Energy Markets

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“…The first studies following this approach rely on very strong assumptions, like presuming that the convenience yield is a function of the spot price and the interest rate is constant (Brennan & Schwartz, 1985), whereas more recent studies consider an increasing number of stochastic factors to explain uncertainty and model the drift of the oil price stochastic process according to the mean reversion detected in oil spot price movements (Cortazar & Schwartz, 2003;Gibson & Schwartz, 1990;Schwartz, 1997). Another recent contribution to the oil prices forecasting literature proposes a forecasting model of monthly crude spot prices which considers industrial petroleum inventory levels as the main explanatory variable (Ye, et al, 2005). Forecast exercises proposed in the last years appear to show good in-sample and out-of-sample performance.…”

Section: Oil Prices Forecastingmentioning

confidence: 99%

Forecasting oil price movements: Exploiting the information in the futures market

Coppola¹

2007

Journal of Futures Markets

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Relying on the cost of carry model, the long-run relationship between spot and futures prices is investigated and the information implied in these cointegrating relationships is used to forecast out of sample oil spot and futures price movements. To forecast oil price movements, a vector error correction model (VECM) is employed, where the deviations from the long-run relationships between spot and futures prices constitute the equilibrium error. To evaluate forecasting performance, the random walk model (RWM) is used as a benchmark. It was found that (a) in-sample, the information in the futures market can explain a sizable portion of oil price movements; and (b) out-of-sample, the VECM outperforms the RWM in forecasting price movements of 1-month futures contracts.

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“…Some statisticalbased models have been widely used for crude oil prices forecasting. Typical models include the probabilistic model (Abramson and Finizza, 1995), econometric structural models (Huntington, 1994;Ye et al, 2002Ye et al, , 2005Ye et al, , 2006, co-integration analysis (Gulen, 1998), vector auto-regression models (VAR) (Mirmirani and Li, 2004), error correction models (ECM) (Lanza et al, 2005), auto-regressive integrated moving average (ARIMA) (Yu et al, 2008) and semi-parametric approach based on GARCH properties (Morana, 2001). Usually, these models can provide good prediction results when the crude oil price series under study is linear or near linear.…”

Section: Introductionmentioning

confidence: 99%

Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting

Zhang

Wang

2017

EURASIA J MATH SCI T

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Crude oil price forecasting is one of the most important topics in the field of energy research. Accordingly, numerous methods such as statistical, econometrical and intelligent approaches are applied for crude oil price forecasting. In this paper, a typical competitive learning algorithm, support vector machine (SVM), is empirically investigated to verify the feasibility and potentiality of SVM in crude oil price forecasting. For this purpose, five different prediction models, feed-forward neural networks (FNN), auto-regressive integrated moving average (ARIMA) model, fractional integrated ARIMA (ARFIMA) model, Markov-switching ARFIMA (MS-ARFIMA) model, and random walk (RW) model are used in the study. Experimental results obtained show that the SVM model outperforms the other five methods, implying that it is a fairly good candidate for crude oil price forecasting in terms of either one-step prediction or multi-step prediction.

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A monthly crude oil spot price forecasting model using relative inventories

Cited by 125 publications

References 7 publications

Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting

Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting

Forecasting oil price movements: Exploiting the information in the futures market

Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting

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